multi-agent-ralph-loop
🥈SilverMulti-Agent AI Orchestration with GLM-4.7, Claude, Codex, and Gemini. Quality gates, memory system, and 67 hooks. Automates complex workflows, ensures code quality, and integrates with CI/CD pipelines. Benefits operations teams by reducing manual oversight and improving process reliability.
123130Updated 2mo ago
Intermediate30min to implementautomation
Saves ~300 min per use
Quick InstallView Source
git clone https://github.com/alfredolopez80/multi-agent-ralph-loop.gitWorks with:
Claude
Overview
About This Skill
Ralph Loop is an autonomous orchestration framework that extends Claude Code with a structured memory system inspired by the Method of Loci (MemPalace technique). It decomposes complex tasks using Aristotle First Principles analysis, executes independent work in parallel across 6 specialized agent teammates (coder, reviewer, tester, researcher, frontend, security), and validates all output through 4-stage blocking quality gates covering correctness, quality, security, and consistency. The framework includes a 4-layer memory architecture (L0-L3) that persists learned rules in an Obsidian vault, automatic learning pipelines that graduate universal patterns to global scope, and lifecycle hooks for validation and security enforcement. Built for development teams and operations that need reliable, auditable automation of complex multi-step workflows with minimal manual oversight.
How to Use
["Prepare your workflow configuration: Create an `AGENT_CONFIG_FILE` (JSON/YAML) defining roles, memory hooks, and quality gates. Use the template: `{ \"agents\": { \"glm-4.7\": { \"role\": \"orchestrator\", \"hooks\": [\"plan_approved\", \"deploy_triggered\"] }, ... }, \"quality_gates\": { \"test_coverage\": 80, \"security_scan\": \"critical\" } }`.","Initialize the multi-agent system: Run `ralph-loop --init --config AGENT_CONFIG_FILE.json`. This sets up the memory system, hooks, and agent roles. Verify initialization with `ralph-loop --status`.","Execute the workflow: Use `ralph-loop --task \"Deploy user_auth module\" --input \"api/handlers.py,tests/auth_tests.py\"`. Monitor progress in real-time via the CLI or dashboard (e.g., `ralph-loop --monitor`).","Validate and integrate: After completion, check the `quality_gates` in the output. If all pass, use `ralph-loop --deploy --pipeline \"devops-pipeline-123\"` to push changes to your CI/CD system. For failures, review the `memory_hooks` to debug issues.","Optimize iteratively: After each run, analyze the `Agent Collaboration Score` and `Error Rate`. Adjust hooks or roles in the config file to improve efficiency. Use `ralph-loop --analyze --metrics \"collaboration_score,error_rate\"` to generate insights."]
Use Cases
Automate full-cycle development workflows with parallel code generation, testing, and security review
Execute complex CI/CD pipeline tasks with built-in quality gates and consistency checks
Build autonomous research and experimentation loops with multi-agent cross-validation
Perform systematic security audits across codebases with OWASP and semgrep integration
Tags
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/alfredolopez80/multi-agent-ralph-loopRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Orchestrate a multi-agent workflow to [TASK] using GLM-4.7, Claude, Codex, and Gemini. Follow these steps: 1) Initialize agents with [ROLES] and [MEMORY_HOOKS]. 2) Execute the workflow with [INPUT_DATA] and apply [QUALITY_GATES]. 3) Monitor progress via [MONITORING_HOOKS] and log outputs to [LOG_FILE]. 4) Validate results against [CRITERIA] and trigger [AUTOMATION_HOOKS] if thresholds are met. 5) Integrate final output into [CI/CD_PIPELINE]. Use [AGENT_CONFIG_FILE] for setup.
Example Output
```json
{
"workflow_id": "ralph-loop-20241015-1422",
"status": "completed",
"agents": {
"glm-4.7": {
"role": "orchestrator",
"output": {"plan": "validated", "next_steps": ["code_review", "test_automation"]},
"memory_hook": "plan_approved"
},
"claude": {
"role": "code_reviewer",
"output": {"issues": [{"severity": "high", "file": "api/handlers.py", "line": 42}], "suggestions": ["refactor_logic", "add_unit_tests"]},
"memory_hook": "review_completed"
},
"codex": {
"role": "code_fixer",
"output": {"changes": [{"file": "api/handlers.py", "diff": "- old_logic\n+ new_logic"}], "status": "applied"},
"memory_hook": "fixes_applied"
},
"gemini": {
"role": "test_automator",
"output": {"test_coverage": "92%", "failed_tests": ["test_edge_case_12"], "status": "partial_pass"},
"memory_hook": "tests_executed"
}
},
"quality_gates": {
"code_review": "passed",
"test_coverage": "85% (threshold: 80%)",
"security_scan": "no_critical_issues"
},
"automation_hooks": {
"deploy_to_staging": "triggered",
"notify_team": "slack_channel_#devops"
},
"ci_cd_integration": {
"pipeline": "devops-pipeline-123",
"status": "deployed_to_staging",
"logs": "https://ci.example.com/builds/123/logs"
},
"next_actions": [
"Monitor staging deployment for 30 minutes",
"Run post-deployment smoke tests",
"Update documentation if changes were significant"
]
}
```
### Key Metrics:
- **Total Execution Time:** 12 minutes 45 seconds
- **Agent Collaboration Score:** 94% (based on handoff efficiency)
- **Error Rate:** 0% (no manual interventions required)
- **Memory System Usage:** 67 hooks activated (100% coverage for this workflow)
### Observations:
The multi-agent system efficiently handled the deployment of the new `user_auth` module. Codex resolved 3 critical code issues identified by Claude, while Gemini ensured 92% test coverage—exceeding the 80% threshold. The GLM-4.7 orchestrator dynamically adjusted priorities when the security scan flagged a minor vulnerability in `auth_utils.py`, triggering a re-review by Claude. All quality gates passed, and the CI/CD pipeline automatically deployed the changes to staging. The memory system logged every decision point, enabling full traceability for compliance audits.Apply to these tools
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